Key Genes And Pathways Controlled By E2F1 In Human Castration-Resistant Prostate Cancer Cells

Identification of a prognostic gene signature in patients with cisplatin resistant squamous cell lung cancer

Background

In the absence of targeted mutations and immune checkpoints, platinum-based chemotherapy remains a gold standard agent in the treatment of patients with lung squamous cell carcinoma (LUSC). However, cisplatin resistance greatly limits its therapeutic efficacy and presents challenges in the treatment of lung cancer patients. Therefore, the potential clinical needs for this research focus on identifying novel molecular signatures to further elucidate the underlying mechanisms of cisplatin resistance in LUSC. A growing body of evidence indicates that alternative splicing (AS) events significantly influence the tumor progression and survival of patients with LUSC. However, there are few systematic analyses of AS reported in LUSC. This study aims to explore the role of messenger RNA (mRNA), microRNA (miRNA), and AS in predicting prognosis in patients with cisplatin-resistant LUSC and provide potential therapeutic targets and drugs.

Methods

Gene expression and miRNA expression, using RNA sequencing (RNA-seq), and SpliceSeq data were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to construct predictive models. Kaplan-Meier survival analyses were used to evaluate patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) conducted via the R package "GSEAbase" was used to evaluate the immune-related characteristics. Immunohistochemistry was used to examine protein expression. The Connectivity Map (CMap) database was used to screen for potential drugs. The 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay was used to determine and calculate the half-maximal inhibitory concentration (IC50) of the drugs, sulforaphane and parthenolide.

Results

In this study, bioinformatics were used to identify mRNAs, miRNAs, and AS events related to response to cisplatin and to establish an integrated prognostic signature for 70 patients with LUSC and cisplatin resistance. The prognostic signature served as an independent prognostic factor with high accuracy [hazard ratio (HR) =2.346, 95% confidence interval (CI): 1.568-3.510; P<0.001], yielding an area under the curve (AUC) of 0.825, 0.829, and 0.877 for 1-, 3-, and 5-year survival, respectively. It also demonstrated high predictive performance in this cohort of patients with LUSC, with an AUC of 0.734, 0.767, and 0.776 for 1-, 3-, and 5-year survival, respectively. This integrated signature was also found to be an independent indicator among conventional clinical features (HR =2.288, 95% CI: 1.547-3.383; P<0.001). In addition, we analyzed the correlation of the signature with immune infiltration and identified several small-molecule drugs that had the potential to improve the survival of patients with LUSC.

Conclusions

This study provides a framework for the mRNA-, miRNA-, and AS-based evaluation of cisplatin response and several potential therapeutic drugs for targeting cisplatin resistance in LUSC. These findings may serve as a theoretical basis for the clinical alleviation of cisplatin resistance and thus help to improve treatment responses to chemotherapy in patients with LUSC.

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC

Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is a central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise to elucidate markers of heterogeneous treatment response and salvage therapies for CRPC patients. Focusing on the de novo role of MYC as a marker of Enzalutamide resistance, here we reconstruct a CRPC-specific mechanism-centric regulatory network, connecting molecular pathways with their upstream transcriptional regulatory programs. Mining this network with signatures of Enzalutamide response identifies NME2 as an upstream regulatory partner of MYC in CRPC and demonstrates that NME2-MYC increased activities can predict patients at risk of resistance to Enzalutamide, independent of co-variates. Furthermore, our experimental investigations demonstrate that targeting MYC and its partner NME2 is beneficial in Enzalutamide-resistant conditions and could provide an effective strategy for patients at risk of Enzalutamide resistance and/or for patients who failed Enzalutamide treatment.

Tissue-specific biological aging predicts progression in prostate cancer and acute myeloid leukemia

Introduction

Chronological aging is a well-recognized diagnostic and prognostic factor in multiple cancer types, yet the role of biological aging in manifesting cancer progression has not been fully explored yet.

Methods

Given the central role of chronological aging in prostate cancer and AML incidence, here we investigate a tissue-specific role of biological aging in prostate cancer and AML progression. We have employed Cox proportional hazards modeling to associate biological aging genes with cancer progression for patients from specific chronological aging groups and for patients with differences in initial cancer aggressiveness.

Results

Our prostate cancer-specific investigations nominated four biological aging genes (CD44, GADD45B, STAT3, GFAP) significantly associated with time to disease progression in prostate cancer in Taylor et al. patient cohort. Stratified survival analysis on Taylor dataset and validation on an independent TCGA and DKFZ PRAD patient cohorts demonstrated ability of these genes to predict prostate cancer progression, especially for patients with higher Gleason score and for patients younger than 60 years of age. We have further tested the generalizability of our approach and applied it to acute myeloid leukemia (AML). Our analysis nominated three AML-specific biological aging genes (CDC42EP2, CDC42, ALOX15B) significantly associated with time to AML overall survival, especially for patients with favorable cytogenetic risk score and for patients older than 56 years of age.

Discussion

Comparison of the identified PC and AML markers to genes selected at random and to known markers of progression demonstrated robustness of our results and nominated the identified biological aging genes as valuable markers of prostate cancer and AML progression, opening new avenues for personalized therapeutic management and potential novel treatment investigations.

Identification of novel prognostic circRNA biomarkers in circRNA-miRNA-mRNA regulatory network in gastric cancer and immune infiltration analysis

BACKGROUND

Gastric cancer (GC) carries significant morbidity and mortality globally. An increasing number of studies have confirmed that circular RNA (circRNA) is tightly associated with the carcinogenesis and development of GC, especially acting as a competing endogenous RNA for miRNAs.

OBJECTIVE

Our study aimed to construct the circRNA-miRNA-mRNA regulatory network and analyze the function and prognostic significance of the network using bioinformatics tools.

METHODS

We first downloaded the GC expression profile from the Gene Expression Omnibus database and identified differentially expressed genes and differentially expressed circRNAs. Then, we predicted the miRNA-mRNA interaction pairs and constructed the circRNA-miRNA-mRNA regulatory network. Next, we established a protein-protein interaction network and analyzed the function of these networks. Finally, we primarily validated our results by comparison with The Cancer Genome Atlas cohort and by performing qRT-PCR.

RESULTS

We screened the top 15 hub genes and 3 core modules. Functional analysis showed that in the upregulated circRNA network, 15 hub genes were correlated with extracellular matrix organization and interaction. The function of downregulated circRNAs converged on physiological functions, such as protein processing, energy metabolism and gastric acid secretion. We ascertained 3 prognostic and immune infiltration-related genes, COL12A1, COL5A2, and THBS1, and built a nomogram for clinical application. We validated the expression level and diagnostic performance of key prognostic differentially expressed genes.

CONCLUSIONS

In conclusion, we constructed two circRNA-miRNA-mRNA regulatory networks and identified 3 prognostic and screening biomarkers, COL12A1, COL5A2, and THBS1. The ceRNA network and these genes could play important roles in GC development, diagnosis and prognosis.

Increased Kremen2 predicts worse prognosis in colon cancer

Background: Colon cancer (CC) is the fifth most prevalent cancer around the globe and poses a major risk to human health. Even though Kremen2 serves as a prognostic indicator in individuals with malignant tumours, its role in evaluating the prognosis of individuals with colon cancer has not been confirmed. Methods: Here, we examined the protein expression of Kremen2 in CC tissues and paired adjacent normal tissues by immunohistochemistry (IHC), then analyzed the clinical and RNA-seq data presented in The Cancer Genome Atlas (TCGA) database to confirm the relationship between Kremen2 levels and CC. In addition, the associations between Kremen2 mRNA expression and infiltrating immune cells were examined. Results: The study showed that the mRNA expression and protein level of Kremen2 were increased in CC tissues compared with adjacent normal tissues. According to Kaplan-Meier analysis, high Kremen2 expression in CC was linked to poor overall survival and progression-free survival. Clinical correlation analysis highlighted that a high level of Kremen2 expression was strongly linked with tumour progression, particularly lymph node metastasis. Cox regression analysis highlighted that Kremen2 was an independent prognostic indicator for CC. Bioinformatic studies highlighted that Kremen2 might be associated with the immune status in CC. Conclusion: Increased Kremen2 could serve as a potential prognostic CC biomarker.

Identification and validation of an E2F-related gene signature for predicting recurrence-free survival in human prostate cancer

BACKGROUND

It is well-established that biochemical recurrence is detrimental to prostate cancer (PCa). In the present study, we explored the mechanisms underlying PCa progression.

METHODS

Five cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases were used to perform gene set variation analysis (GSVA) between nonrecurrent and recurrent PCa patients. We obtained the intersection of pathway enrichment results and extracted the corresponding gene list. LASSO Cox regression analysis was used to identify recurrence-free survival (RFS)-related significant genes and establish an RFS prediction gene signature and nomogram. MTT and colony formation assays were conducted to validate our findings.

RESULTS

The E2F signaling pathway was activated in recurrent PCa patients compared to nonrecurrent patients. We established an E2F-related gene signature for RFS prediction based on the four identified E2F-related genes (CDKN2C, CDKN3, RACGAP1, and RRM2) using LASSO Cox regression in the Memorial Sloan Kettering Cancer Center (MSKCC) cohort. The risk score of each patient in MSKCC was calculated based on the expression levels of CDKN2C, CDKN3, RACGAP1, and RRM2. PCa patients with low-risk scores exhibited higher RFS than those with high-risk scores. Receiver operating characteristic (ROC) curve analysis validated the good performance and prognostic accuracy of the E2F-related gene signature, which was validated in the TCGA-prostate adenocarcinoma (TCGA-PRAD) cohort. Compared to patients with low Gleason scores and early T stages, PCa patients with high Gleason scores and advanced T stages had high-risk scores. Moreover, the E2F-related gene signature-based nomogram yielded good performance in RFS prediction. Functional experiments further confirmed these results.

CONCLUSIONS

The E2F signaling pathway is associated with biochemical recurrence in PCa. Our established E2F-related gene signature and nomogram yielded good accuracy in predicting the biochemical recurrence in PCa.

Transcription factor p53-mediated activation of miR-519d-3p and downregulation of E2F1 attenuates prostate cancer growth and metastasis

Prostate cancer (PCa) is a commonly diagnosed malignancy in men. The transcription factor p53, a well-known cancer suppressor, has been extensively analyzed in the progression of many tumor types, but its involvement in PCa remains not fully understood. Hence, this study aims to explore the possible molecular mechanism underlying p53 in the growth and metastasis of PCa. Based on bioinformatics analysis findings of GEPIA and starBase databases, p53 was demonstrated to be involved in the development of PCa by transcriptionally activating microRNA-519d-3p (miR-519d-3p) expression to suppress the expression of E2F transcription factor 1 (E2F1) and CD147. In order to verify this finding, clinically-obtained PCa tumor tissues were enrolled and commercially-purchased PCa cell lines were used to detect the cell viability, cycle, and apoptosis, as well as invasion and migration by CCK-8, flow cytometry, and Transwell assays respectively. The results of clinical tissue experiments and in vitro cell experiments showed that miR-519d-3p and p53 were poorly-expressed in PCa tissues and cell lines, while E2F1 was highly-expressed. Overexpression of miR-519d-3p led to inhibited PCa cell proliferation, invasion and migration, and p53 overexpression was found to promote miR-519d-3p expression to suppress the malignant characteristics of PCa cells, while the additional E2F1 overexpression restored the malignant traits. Moreover, ChIP analysis and dual-luciferase reporter assay confirmed the interactions among p53, miR-519d-3p, and E2F1. Mechanistically, it was found that p53 transcriptionally activated miR-519d-3p to suppress E2F1 expression. Finally, the in vitro results were further validated by in vivo experiments, which showed that miR-519d-3p prevents tumorigenesis and lymph node metastasis of PCa in nude mice via negatively regulation of E2F1 and CD147. Taken together, the findings uncover that the transcription factor p53 could upregulate miR-519d-3p expression to directly suppress the expression of E2F1, thus inhibiting PCa growth and metastasis. It highlights a novel therapeutic strategy against PCa based on the p53/miR-519d-3p/E2F1 regulatory pathway.

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Prostate Cancer

Background

We planned to uncover the cancer stemness-related genes (SRGs) in prostate cancer (PCa) and its underlying mechanism in PCa metastasis.

Methods

We acquired the RNA-seq data of 406 patients with PCa from the TCGA database. Based on the mRNA stemness index (mRNAsi) calculated by one-class logistic regression (OCLR) algorithm, SRGs in PCa were extracted by WGCNA. Univariate and multivariate regression analyses were applied to uncover OS-associated SRGs. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Pearson's correlation analysis were performed to discover the possible mechanism of PCa metastasis. The significantly correlated transcription factors of OS-associated SRGs were also identified by Pearson's correlation analysis. ChIP-seq was applied to validate the binding relationship of TFs and OS-associated SRGs and spatial transcriptome and single-cell sequencing were performed to uncover the location of key biomarkers expression. Lastly, we explored the specific inhibitors for SRGs using CMap algorithm.

Results

We identified 538 differentially expressed genes (DEGs) between non-metastatic and metastatic PCa. Furthermore, OS-associated SRGs were identified. The Pearson correlation analysis revealed that FOXM1 was significantly correlated with NEIL3 (correlation efficient =0.89, p < 0.001) and identified hallmark_E2F_targets as the potential pathway mechanism of NEIL3 promoting PCa metastasis (correlation efficient =0.58, p < 0.001). Single-cell sequencing results indicated that FOXM1 regulating NEIL3 may get involved in the antiandrogen resistance of PCa. Rottlerin was discovered to be a potential target drug for PCa.

Conclusion

We constructed a regulatory network based on SRGs associated with PCa metastasis and explored possible mechanism.

WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of the present study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in ‘superoxide anion generation’ and ‘complement and coagulation cascades’. Finally, we validated the key genes by Gene Expression Profiling Interactive Analysis (GEPIA) and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in the present study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.